What Is the Difference Between L1 (Lasso) and L2 (Ridge) Regression?

Learn the key differences between L1 (Lasso) and L2 (Ridge) regression techniques, including their penalties and effects on model coefficients.

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L1 regression (Lasso) uses absolute values of coefficient magnitudes for penalty, leading to sparse solutions by potentially reducing some coefficients to zero. L2 regression (Ridge) employs the square of the magnitudes for penalty, resulting in small but non-zero coefficients, thus improving model stability by addressing multicollinearity without eliminating predictors.

FAQs & Answers

  1. What is L1 regression (Lasso)? L1 regression, also known as Lasso, applies a penalty based on the absolute values of the coefficients, promoting sparsity by shrinking some coefficients to zero and effectively performing feature selection.
  2. How does L2 regression (Ridge) differ from L1 regression? L2 regression, or Ridge, penalizes the square of the coefficient magnitudes, which shrinks coefficients towards zero but usually keeps all predictors in the model, enhancing stability especially in the presence of multicollinearity.
  3. When should I use L1 versus L2 regression? Use L1 regression when you want automatic feature selection through sparse models, and L2 regression when you want to improve model stability without eliminating predictors, especially if multicollinearity is a concern.

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